Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Impact of trophic ecologies on the whereabouts of nematodes in soil
Quist, Casper W. - \ 2017
Wageningen University. Promotor(en): Jaap Bakker, co-promotor(en): Hans Helder. - Wageningen : Wageningen University - ISBN 9789463430814 - 129
nematoda - spatial distribution - soil fauna - biota - trophic levels - food webs - soil ecology - soil biology - farming systems - soil types (ecological) - geostatistics - ruimtelijke verdeling - bodemfauna - trofische graden - voedselwebben - bodemecologie - bodembiologie - bedrijfssystemen - bodemtypen (ecologisch) - geostatistiek

Soil life is highly diverse, and ecologically intricate due to myriad of biotic interactions that take place. Terrestrial nematodes have a high potential to serve as an effective and policy-relevant indicator group for ecosystem functioning and soil biodiversity. The work described in this thesis contributed to the robust mapping of nematode communities at scales relevant in both agronomic and environmental contexts. The overarching aim of the work described in this thesis was to contribute to a sound exploration of the potential of nematode communities as an indicator group for the biological condition of soils. Therefore, the distributions of a wide range of nematode taxa were studied, within and between trophic groups and in soils conditioned by various plant species and/or farming systems.

In Chapter 2 nematode taxon-specific qPCR assays were used to pinpoint responses of nematode communities to invasive plant species Solidago gigantea in two invaded ecosystems: semi-natural grasslands and riparian floodplains. Nematode communities and fungal biomass were examined in adjacent invaded and uninvaded patches. The dominant presence of the invasive plant causes a decrease of plant species-richness and diversity, and an about twofold increase of fungal biomass. Only the density of a single group of fungivorous nematodes (Aphelenchoididea) increased, whereas the densities of two other, phylogenetically distinct lineages of fungivorous nematodes, Aphelenchidae and Diphtherophoridae, were unaffected by the local increase in fungal biomass. Apparently S. gigantea induces a local asymmetric boost of the fungal community, and only Aphelenchoididae were able to benefit from this change induced by the invasive plant.

In Chapter 3 the outcome is shown of a test whether farming system effects are mirrored in compositional changes in nematode communities. The long-term impact of three farming systems (conventional, integrated and organic) on nematode communities was investigated at the Vredepeel, an experimental farm in the southeastern part of The Netherlands. The results showed that organic farming causes specific shifts in nematode community composition, exceeding the usually large crop-related assemblage shifts. Strongest effects were observed for the (putative) bacterivore Prismatolaimus, which was relatively common in organic fields and nearly absent in conventional and integrated farming. A reverse effect was observed for Pristionchus; this necromenic bacterivore and facultative predator made up about 7 – 21% of the total nematode community in integrated and conventional farming, whereas it was nearly absent from organic fields. The observed farming system effects suggest that specific nematode taxa might be indicative for the impact of farming practices on soil biota. Knowledge of spatial distribution patterns of soil organisms with distinct trophic preferences will contribute to our understanding of factors that maintain and regulate soil biodiversity, and is essential information to design soil sampling strategies with predictable accuracies.

Chapter 4 deals with microscale patchiness of 45 nematode taxa (at family, genus or species-level) in arable fields and semi-natural grasslands, on marine clay, river clay or sandy soils. Contrary to our expectations, an increase of the number of cores per composite sample above 3, did not result in more accurate detection for any of the taxa under investigation (range of number of cores per composite sample: 3, 6, 12 or 24). Neither system nor soil type did influence microscale distribution. The insights in the spatial distribution of nematodes at microscale presented here, sheds light on the impact of trophic preferences on the spatial distribution of individual nematode taxa, and will allow for the design of statistically sound soil sampling strategies.

Chapter 5 shows belowground distribution patterns of 48 nematode taxa in 12 visually homogeneous fields (each 100 x 100 m) on three soil types (marine clay, river clay and sand) and two land-use types (arable and natural grasslands) across the Netherlands. Over 35,000 nematode-taxon specific qPCR assays allowed us to quantitative analyse nematode taxa at family, genus or species level in over 1,200 soil samples. A sampling scheme was optimized for Bayesian geostatistical analysis (Integrated nested Laplace approximations; INLA). Multivariate analysis show soil type and land-use related differences in the nematode community composition, which underline the effects of environmental filtering and niche partitioning of nematodes. All individual nematode taxa together show a wide range of degrees of spatial variabilities were found (expressed by the range-parameter and the spatial variance parameter (s2spatial). No general effects were detected of soil characteristics or nematode traits (cp-value, trophic group, weight) on the spatial distribution parameters. The relatively high percentages of unexplained spatial variability – 92.5% of the variation for the range-parameter and 74% for spatial variance (s2spatial) – point at a major role of stochasticity for variability of nematode densities within fields. This study adds empirical evidence that distribution patterns of terrestrial nematodes, in areas without noticeable gradients, are driven by neutral / stochastic processes, within the boundaries set by the environment.

In the final Chapter 6, I discuss the opportunities and challenges of the use of molecular tools in soil ecological research, the impact of trophic preferences on the whereabouts of nematodes, the use of nematode communities as indicator for soil condition and how this might be developed and applied to facilitate more sustainable ecosystem management.

Effects of variable mean target strength on estimates of abundance: the case of Atlantic mackerel (Scomber scombrus)
Scoulding, Ben ; Gastauer, Sven ; Maclennan, David N. ; Fassler, S.M.M. ; Copland, Phillip ; Fernandes, Paul G. - \ 2017
ICES Journal of Marine Science 74 (2017)3. - ISSN 1054-3139 - p. 822 - 831.
Atlantic mackerel - biomass estimation - geostatistics - scattering properties - target strength
Atlantic mackerel Scomber scombrus is a small pelagic, migratory fish which supports commercial fisheries. These fish school and are detectable using echosounders, yet fishery-independent estimates of their abundance in the North East Atlantic do not consider acoustic data. Accurate estimates of mean target strength (TS) are presently limiting echo-integration surveys from providing useful estimates of Atlantic mackerel abundance and distribution. This study provides TS estimates for in situ mackerel from multi-frequency split-beam echosounder measurements. TS equals 52.79 dB at 18 kHz, 59.60 dB at 38 kHz, 55.63 dB at 120 kHz, and 53.58 dB at 200 kHz, for a mean mackerel
total length¼33.3 cm. These values differ from those currently assumed for this the sensitivity of acoustically estimated mackerel biomass around the Shetland Islands, Scotland, in 2014, to various estimates of TS. Confidence limits were obtained using geostatistics accounting for coverage and spatial autocorrelation. Stock biomasses, estimated from 38 and 200 kHz data, differed by 10.5%, and stock distributions were similar to each other and to the estimates from an independent stock assessment. Because mackerel backscatter at 38 kHz is dominated by echoes from the flesh and may have similarities to echoes from fish with swimbladders, and backscatter at 200 kHz is dominated by relatively stable echoes from the backbone, we recommend using 200 kHz data for
estimates of Atlantic mackerel biomass.
Spatial Sampling Design for Estimating Regional GPP With Spatial Heterogeneities
Wang, J.H. ; Ge, Y. ; Heuvelink, G.B.M. ; Zhou, C.H. - \ 2014
IEEE Geoscience and Remote Sensing Letters 11 (2014)2. - ISSN 1545-598X - p. 539 - 543.
optimization - geostatistics - strategies - maize
The estimation of regional gross primary production (GPP) is a crucial issue in carbon cycle studies. One commonly used way to estimate the characteristics of GPP is to infer the total amount of GPP by collecting field samples. In this process, the spatial sampling design will affect the error variance of GPP estimation. This letter uses geostatistical model-based sampling to optimize the sampling locations in a spatial heterogeneous area. The approach is illustrated with a real-world application of designing a sampling strategy for estimating the regional GPP in the Babao river basin, China. By considering the heterogeneities in the spatial distribution of the GPP, the sampling locations were optimized by minimizing the spatially averaged interpolation error variance. To accelerate the optimization process, a spatial simulated annealing search algorithm was employed. Compared with a sampling design without considering stratification and anisotropies, the proposed sampling method reduced the error variance of regional GPP estimation.
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
Kilibarda, M. ; Hengl, T. ; Heuvelink, G.B.M. ; Graler, B. ; Pebesma, E. ; Tadic, M.P. ; Bajat, B. - \ 2014
Journal of Geophysical Research: Atmospheres 119 (2014)5. - ISSN 2169-897X - p. 2294 - 2313.
daily climate extremes - space-time climate - data set - spatial interpolation - surface temperature - daily precipitation - air-temperature - part ii - variability - geostatistics
Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points Global spatio-temporal regression-kriging daily temperature interpolation Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures Time series of MODIS 8 day images as explanatory variables in regression part
Incorporating DEM Uncertainty in Coastal Inundation Mapping
Leon, J.X. ; Heuvelink, G.B.M. ; Phinn, S.R. - \ 2014
PLoS ONE 9 (2014)9. - ISSN 1932-6203
sea-level rise - climate-change - spatial prediction - airborne lidar - elevation data - error - geostatistics - adaptation - topography - simulation
Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.
Expert knowledge in geostatistical inference and prediction
Truong, N.P. - \ 2014
Wageningen University. Promotor(en): Peter de Ruiter, co-promotor(en): Gerard Heuvelink. - Wageningen : Wageningen University - ISBN 9789462570283 - 156
geostatistiek - biometrie - ruimtelijke statistiek - statistische inferentie - voorspelling - bayesiaanse theorie - deskundigen - kriging - geostatistics - biometry - spatial statistics - statistical inference - prediction - bayesian theory - experts

Geostatistics provides an efficient tool for mapping environmental variables from observations and layers of explanatory variables. The number and configuration of the observations importantly determine the accuracy of geostatistical inference and prediction. Data collection is costly, and coarse sampling may lead to large uncertainties in interpolated maps. In such case, additional information may be gathered from experts who are knowledgeable about the spatial variability of environmental variables. Statistical expert elicitation has gradually become a mature research field and has proved to be able to extract from experts reliable information to form a sound scientific database. In this thesis, expert knowledge has been elicited and incorporated in geostatistical models for inference and prediction. Various extensions to the expert elicitation literature were required to make it suitable for elicitation of spatial data. The use of expert knowledge in geostatistical research is promising, yet challenging.

Soil organic carbon stocks in the Limpopo National Park, Mozambique: Amount, spatial distribution and uncertainty.
Cambule, A. ; Rossiter, D.G. ; Stoorvogel, J.J. ; Smaling, E.M.A. - \ 2014
Geoderma 213 (2014). - ISSN 0016-7061 - p. 46 - 56.
residual maximum-likelihood - optimal sampling schemes - regionalized variables - land-use - terrain attributes - local estimation - data sets - sequestration - geostatistics - variability
Many areas in sub-Saharan African are data-poor and poorly accessible. The estimation of soil organic carbon (SOC) stocks in these areas will have to rely on the limited available secondary data coupled with restricted field sampling. We assessed the total SOC stock, its spatial variation and the causes of this variation in Limpopo National Park (LNP), a data-poor and poorly accessible area in southwestern Mozambique. During a field survey, A-horizon thickness was measured and soil samples were taken for the determination of SOC concentrations. SOC concentrations were multiplied by soil bulk density and A-horizon thickness to estimate SOC stocks. Spatial distribution was assessed through: i) a measure-and-multiply approach to assess average SOC stocks by landscape unit, and ii) a soil-landscape model that used soil forming factors to interpolate SOC stocks from observations to a grid covering the area by ordinary (OK) and universal (UK) kriging. Predictions were validated by both independent and leave-one-out cross validations. The total SOC stock of the LNPwas obtained by i) calculating an area-weighted average from the means of the landscape units and by ii) summing the cells of the interpolated grid. Uncertainty was evaluated by the mean standard error for the measure-and-multiply approach and by the mean kriging prediction standard deviation for the soil-landscape model approach. The reliability of the estimates of total stockswas assessed by the uncertainty of the input data and its effect on estimates. The mean SOC stock from all sample points is 1.59 kg m-2; landscape unit averages are 1.13–2.46 kg m-2. Covariables explained 45% (soil) and 17% (coordinates) of SOC stock variation. Predictions from spatial models averaged 1.65 kg m-2 and are within the ranges reported for similar soils in southern Africa. The validation root mean square error of prediction (RMSEP) was about 30% of the mean predictions for both OK and UK. Uncertainty is high (coefficient of variation of about 40%) due to short-range spatial structure combined with sparse sampling. The range of total SOC stock of the 10,410 km-2 study area was estimated at 15,579–17,908 Gg. However, 90% confidence limits of the total stocks estimated are narrower (5–15%) for the measure-and-multiply model and wider (66–70%) for the soil-landscape model. The spatial distribution is rather homogenous, suggesting levels are mainly determined by regional climate.
Bayesian Area-to-Point Kriging Using Expert Knowledge as Informative Priors
Truong, N.P. ; Heuvelink, G.B.M. ; Pebesma, E. - \ 2014
International Journal of applied Earth Observation and Geoinformation 30 (2014). - ISSN 0303-2434 - p. 128 - 138.
probability-distributions - elicitation - opinion - deconvolution - geostatistics - models
Area-to-point (ATP) kriging is a common geostatistical framework to address the problem of spatial disaggregation or downscaling from block support observations (BSO) to point support (PoS) predictions for continuous variables. This approach requires that the PoS variogram is known. Without PoS observations, the parameters of the PoS variogram cannot be deterministically estimated from BSO, and as a result, the PoS variogram parameters are uncertain. In this research, we used Bayesian ATP conditional simulation to estimate the PoS variogram parameters from expert knowledge and BSO, and quantify uncertainty of the PoS variogram parameters and disaggregation outcomes. We first clarified that the nugget parameter of the PoS variogram cannot be estimated from only BSO. Next, we used statistical expert elicitation techniques to elicit the PoS variogram parameters from expert knowledge. These were used as informative priors in a Bayesian inference of the PoS variogram from BSO and implemented using a Markov chain Monte Carlo algorithm. ATP conditional simulation was done to obtain stochastic simulations at point support. MODIS (Moderate Resolution Imaging Spectroradiometer) atmospheric temperature profile data were used in an illustrative example . The outcomes from the Bayesian ATP inference for the Matérn variogram model parameters confirmed that the posterior distribution of the nugget parameter was effectively the same as its prior distribution; for the other parameters, the uncertainty was substantially decreased when BSO were introduced to the Bayesian ATP estimator. This confirmed that expert knowledge brought new information to infer the nugget effect at PoS while BSO only brought new information to infer the other parameters. Bayesian ATP conditional simulations provided a satisfactory way to quantify parameters and model uncertainty propagation through spatial disaggregation.
Effects of spatial pattern persistence on the performance of sampling designs for regional trend monitoring analyzed by simulation of space-time fields
Brus, D.J. ; Gruijter, J.J. de - \ 2013
Computers and Geosciences 61 (2013). - ISSN 0098-3004 - p. 175 - 183.
geostatistics
The effect of the persistence of spatial patterns on the performance of space-time sampling designs is explored by a simulation study. The performance is evaluated on the basis of the covariance matrix of the two parameters (intercept and slope) of a linear model for the change over time of the spatial means or totals. The evaluated sampling approach is hybrid, i.e. design-based estimation of spatial means from spatial probability samples is combined with time-series modelling of the spatial means. A simulation algorithm is presented for approximating the covariance matrix of the time-series model parameters from a full space-time model. Designs were evaluated on the basis of the determinant of this matrix and the variance of the estimated trend parameter. As a space-time model a sum-metric space-time variogram is used, the parameters of which are chosen such that the persistence of spatial patterns varies from nearly absent to very strong. Based on the extensive simulations, recommendations on the type of space-time design can most easily be made for situations with either very strong or no persistence of spatial patterns. With strong persistence the supplemented panel (Sup) design is recommendable. With no persistence the independent-synchronous (IS) and serially alternating (SA) designs are the best choice. These designs performed well with regard to both quality criteria. With moderate persistence of spatial patterns the choice of design type is more complicated. The IS and static-synchronous (SS) design performed best on one quality criterion, but worst on the other. Therefore, with moderate pattern persistence, the compromise designs, either SuP or SA, can be a good choice, unless one of the two quality criteria has priority. An R script is provided for ex ante evaluation of space-time designs in real-world applications. (C) 2013 Elsevier Ltd. All rights reserved.
A methodology for digital soil mapping in poorly-accessible areas
Cambule, A. ; Rossiter, D.G. ; Stoorvogel, J.J. - \ 2013
Geoderma 192 (2013). - ISSN 0016-7061 - p. 341 - 353.
residual maximum-likelihood - reflectance spectroscopy - shifting cultivation - terrain attributes - spatial prediction - organic-matter - carbon - variogram - scales - geostatistics
Effective soil management requires knowledge of the spatial patterns of soil variation within the landscape to enable wise land use decisions. This is typically obtained through time-consuming and costly surveys. The aim of this study was to develop a cost-efficient methodology for digital soil mapping in poorly-accessible areas. The methodology uses a spatial model calibrated on the basis of limited soil sampling and explanatory covariables related to soil-forming factors, developed from readily available secondary information from accessible areas. The model is subsequently applied in the poorly-accessible areas. This can only be done if the environmental conditions in the poorly-accessible areas are also found in the accessible areas in which the model is developed. This study illustrates the methodology in an exercise to predict soil organic carbon (SOC) concentration in the Limpopo National Park, Mozambique. Readily-available secondary data was used as explanatory variables representing the soil-forming factors. Conditions in the accessible and poorly-accessible areas corresponded sufficiently to allow the extrapolation of the spatial model into the latter. The spatial variation of SOC in the accessible area was mostly described by the sampling cluster (71.5%) and the landscape unit (46.3%). Therefore ordinary (punctual) kriging (OK) and kriging with external drift (KED) based on the landscape unit were used to predict SOC. A linear regression (LM) model using only landscape stratification was used as control. All models were independently validated with test sets collected in both accessible and poorly-accessible areas. In the former the root mean squared error of prediction (RMSEP) was 0.42–0.50% SOC. The ratio between the RMSEP in the poorly-accessible and accessible areas was 0.67–0.72, showing that the methodology can be applied to predict SOC in poorly-accessible areas as successful as in accessible areas. The methodology is thus recommended for areas with similar access problems, especially for baseline studies and for sample design in two-stage surveys
Where and when should sensors move? Sampling using the expected value of information
Bruin, S. de; Ballari, D.E. ; Bregt, A.K. - \ 2012
Sensors 12 (2012)12. - ISSN 1424-8220 - p. 16274 - 16290.
gaussian-processes - soil - optimization - networks - design - geostatistics - prediction
In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision making. This is equivalent to minimizing the aggregated expected misclassification costs over the study area. The method considers measurement error and different costs for class omissions and false class commissions. Constraints imposed by a mobile sensor web are accounted for using cost distances to decide which sensor should move to the next sample location. The method is demonstrated using synthetic examples of static and dynamic phenomena. This allowed computation of the true misclassification costs and comparison with other sampling approaches. The probability of local contamination levels being above a given critical threshold were computed by indicator kriging. In the case of multiple sensors being relocated simultaneously, a genetic algorithm was used to find sets of suitable new measurement locations. Otherwise, all grid nodes were searched exhaustively, which is computationally demanding. In terms of true misclassification costs, the method outperformed random sampling and sampling based on minimisation of the kriging variance.
Comparison of disease patterns assessed by three independent surveys of cassava mosaic virus disease in Rwanda and Burundi
Bouwmeester, H. ; Heuvelink, G.B.M. ; Legg, J.P. ; Stoorvogel, J.J. - \ 2012
Plant Pathology 61 (2012)2. - ISSN 0032-0862 - p. 399 - 412.
spatial prediction - central-africa - eacmv-ug - east - spread - geostatistics
Cassava mosaic disease (CMD) seriously affects cassava yields in Africa. This study compared the spatial distribution of CMD using three independent surveys in Rwanda and Burundi. Geostatistical techniques were used to interpolate the point-based surveys and predict the spatial distributions of different measures of the disease. Correlative relationships were examined for 35 environmental and socio-economic spatial variables of which 31 were correlated to CMD intensity, with the highest correlation coefficients for latitude (-0·47), altitude (-0·36) and temperature (+0·36). The most significant explanatory variables were entered in separate linear regression models for each of the surveys. The models explained 54%, 44% and 22% of the variation in CMD. The residuals of the regression models were interpolated using kriging and added to the regression models to map CMD across both countries. Significant differences were calculated in some areas after correcting for interpolation error. An important explanation of the differences is interaction between the CMD pandemic and the dates of the three surveys. Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error. Improved maps of crop diseases such as CMD could aid targeting of control interventions and thereby contribute to increasing crop yields. This study validated the unique character of each of the survey approaches adopted and underlines the importance of specific interpretation of results for CMD management. The study emphasizes the need for optimization of sampling designs and survey protocols to maximize the potential of regression kriging
Effect of DEM Uncertainty on the Positional Accuracy of Airborne Imagery
Beekhuizen, J. ; Heuvelink, G.B.M. ; Biesemans, J. ; Reusen, I. - \ 2011
IEEE Transactions on Geoscience and Remote Sensing 49 (2011)5. - ISSN 0196-2892 - p. 1567 - 1577.
digital elevation models - imaging spectrometry data - environmental variables - error propagation - c-band - geostatistics - gstat
The geometric and atmospheric processing of airborne imagery is a complex task that involves many correction steps. Geometric correction is particularly challenging because slight movements of the aircraft and small changes in topography can have a great impact on the geographic positioning of the processed imagery. This paper focused on how uncertainty in topography, represented by a digital elevation model (DEM), propagates through the geometric correction process. We used a Monte Carlo analysis, in which, first, a geostatistical uncertainty model of the DEM was developed to simulate a large number of DEM realizations. Next, geometric correction was run for each of the simulated DEMs. The analysis of the corrected images and their variability provided valuable information about the positional accuracy of the corrected image. The method was applied to a hyperspectral image of a mountainous area in Calabria, Italy, by using the Shuttle Radar Topography Mission-DEM as the topographic information source. We found out that the uncertainty varies greatly over the whole terrain and is substantial at large off-nadir viewing angles in the across-track direction. Also, positional uncertainty is larger in rugged terrains. We conclude that Monte Carlo uncertainty propagation analysis is a valuable technique in deriving quality layers that inform end users about the positional accuracy of airborne imagery, and we recommend that it is integrated in the operational processing steps of the Processing and Archiving Facilities.
INTAMAP: The design and implementation of an interoperable automated interpolation web service
Pebesma, E. ; Cornford, D. ; Dubois, G. ; Heuvelink, G.B.M. ; Hristopulos, D. ; Pilz, J. ; Stohlker, U. ; Morin, G. ; Skoien, J.O. - \ 2011
Computers and Geosciences 37 (2011)3. - ISSN 0098-3004 - p. 343 - 352.
data sets - prediction - geostatistics - gstat - space
INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52 degrees North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.
Geostatistische opschaling van concentraties van gewasbeschermingsmiddelen in het Nederlandse oppervlaktewater
Heuvelink, G.B.M. ; Kruijne, R. ; Musters, C.J.M. - \ 2011
Wageningen : Wettelijke Onderzoekstaken Natuur & Milieu (WOt-rapport 115)
pesticiden - waterverontreiniging - monitoring - oppervlaktewater - geostatistiek - nederland - pesticides - water pollution - surface water - geostatistics - netherlands
Metingen van concentraties van gewasbeschermingsmiddelen in het Nederlandse oppervlaktewater worden met een geostatistische methode opgeschaald naar landelijke waarden. De methode maakt gebruik van ruimte-tijd regressie-kriging, waarbij zowel informatie in de metingen zelf als in landsdekkende kaarten van gecorreleerde omgevingsvariabelen wordt benut. De methode berekent eveneens de onzekerheid in de opgeschaalde waarde zodat ook de statistische significantie van temporele trends in landelijke waarden kan worden bepaald. Toepassing van de methode op metribuzin en carbendazim voor de periode 1997-2006 geeft plausibele resultaten die voor metribuzin in alle jaren rond 12 ng/liter liggen en voor carbendazim een dalende trend van 170 ng/liter in 1997 naar 100 ng/liter in 2006 laat zien. De methode is bewerkelijk en stelt hoge eisen aan de beschikbaarheid van data. Belangrijke aandachtspunten voor toekomstig onderzoek zijn statistische validatie van modeluitkomsten, analyse van de gevoeligheid van het model voor gemaakte aannames en de verbeterde verwerking van metingen beneden de kwantificeringslimiet. Trefwoorden: gewasbeschermingsmiddelen, kriging, milieu, regressie, statistische modellering, trend, waterkwaliteit
Vaste grond onder de voeten? Geactualiseerd Bodemkundig Informatie Systeem informeert over onzekerheid
Knotters, M. ; Brus, D.J. ; Heuvelink, G.B.M. ; Kempen, B. ; Vries, F. de; Walvoort, D.J.J. - \ 2010
Bodem 20 (2010)5. - ISSN 0925-1650 - p. 22 - 25.
bodemeigenschappen - informatiesystemen - databanken - bodemgeschiktheid - geostatistiek - soil properties - information systems - databases - soil suitability - geostatistics
Als je vaste grond onder de voeten hebt, dan hoef je niet meer te twijfelen. Maar hoe zeker kun je zijn over de grond onder je voeten? Hoe betrouwbaar is de bodemkaart, schaal 1:50.000?
On the uncertainty of stream networks derived from elevation data: the error propagation approach
Hengl, T. ; Heuvelink, G.B.M. ; Loon, E.E. van - \ 2010
Hydrology and Earth System Sciences 14 (2010)7. - ISSN 1027-5606 - p. 1153 - 1165.
models - geostatistics
DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30 m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise – usually areas of low local relief and slightly convex (0–10 difference from the mean value). In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H>0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delineated streams satisfy the required accuracy level. Such error propagation tool should become a standard functionality in any modern GIS. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the www.geomorphometry.org website and can be easily adopted/adjusted to any similar case study.
A disposition of interpolation techniques
Knotters, M. ; Heuvelink, G.B.M. - \ 2010
Wageningen : Wettelijke Onderzoekstaken Natuur & Milieu (WOt-werkdocument 190) - 86
pesticiden - waterkwaliteit - kriging - tijdreeksen - statistiek - onzekerheid - nauwkeurigheid - natuur - milieu-analyse - geostatistiek - interpolatie - pesticides - water quality - time series - statistics - uncertainty - accuracy - nature - environmental analysis - geostatistics - interpolation
A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method.
The influence of vegetation cover on the spectroscopic estimation of soil properties
Bartholomeus, H. - \ 2009
Wageningen University. Promotor(en): Michael Schaepman, co-promotor(en): Lammert Kooistra. - [S.l. : S.n. - ISBN 9789085854487 - 144
bodemeigenschappen - vegetatie - spectroscopie - schatting - koolstof - landbouwgronden - ijzer - bodemchemie - geostatistiek - soil properties - vegetation - spectroscopy - estimation - carbon - agricultural soils - iron - soil chemistry - geostatistics
Voor het bepalen van de kwaliteit van de bodem als hulpbron is er behoefte aan een regelmatige bepaling van de chemische en fysische eigenschappen, zowel in ruimte als tijd. Kwantitatieve schatting van de exacte hoeveelheid, ruimtelijke verdeling en temporele verandering van bodemeigenschappen is nog steeds een uitdaging. Het onderwerp van dit proefschrift is hoe spectrale reflectie informatie gelinkt kan worden aan bodemeigenschappen
De seizoensfluctuatie van de grondwaterstand in natuurgebieden vanaf 1985 in kaart gebracht
Hoogland, T. ; Heuvelink, G.B.M. ; Knotters, M. - \ 2008
Wageningen : Wettelijke Onderzoekstaken Natuur & Milieu (WOt-rapport 89) - 60
bodemwater - grondwaterstand - cartografie - natuurbescherming - seizoenen - seizoenvariatie - geostatistiek - verdroging (milieu) - soil water - groundwater level - mapping - nature conservation - seasons - seasonal variation - geostatistics - groundwater depletion
Grondwaterafhankelijke ecosystemen in Nederland worden bedreigd door de verlaging van de freatische grondwaterstand. Beschikbare informatie over de grondwaterstand is ontoereikend en achterhaald. Gedetailleerde informatie over grondwater-standen is gewenst, vooral voor natuurreservaten met grondwaterafhankelijke vegetatietypes. Sinds 1980 zijn 35.000 schattingen van seizoensfluctuatie van grondwaterstanden in natuurgebieden verzameld. Met deze waarnemingen is met een geostatistische interpolatie in ruimte en tijd de seizoensfluctuatie van grondwaterstanden tussen 1980 en 2007 in kaart gebracht. Kaarten van de voorspelde gemiddelde grondwaterstand en de nauwkeurigheid van deze voorspellingen zijn gebruikt om gebieden te identificeren waar het grondwater te diep zit voor grondwaterafhankelijke ecosystemen. Veranderingen in de grondwaterstand in de afgelopen 25 jaar op de nationale en provinciale schaal zijn gekwantificeerd. Trefwoorden: verdroging, grondwaterstand, natuur, grondwaterafhankelijk, ruimte-tijd geostatistiek, kaarten, nauwkeurigheid
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